Methods and systems for treating heart instability

ABSTRACT

A system and method for assessing risk associated with a suspected heart rhythm disorder includes at least one sensor for generating a signal received from a beating human heart over a plurality of time segments, and an analytic engine that receives the signal and calculates a change in the signal among at least a first time segment and a second time segment in response to a change in at least one of rate and regularity induced in the beating human heart. The analytic engine generates a risk score for the heart rhythm disorder based at least in part on the change in the signal. The system may further be configured to control modification of tissue of the beating human heart based on the risk score.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 15/193,105,filed Jun. 27, 2016, issued as patent Ser. No. 10/136,860, which is acontinuation of application Ser. No. 14/135,472, filed Dec. 19, 2013,issued as U.S. Pat. No. 9,393,425, which is a continuation ofapplication Ser. No. 12/454,181, filed May 12, 2009, issued as U.S. Pat.No. 8,676,303, which claims the benefit of priority of ProvisionalApplication No. 61/052,970, filed May 13, 2008. Each of the identifiedapplications is incorporated herein by reference in its entirety.

GOVERNMENT RIGHTS

The present invention was made with government support under GrantsHL070529 and HL083359 awarded by the National Institutes of Health. TheGovernment has certain rights in the invention.

BACKGROUND Field of the Invention

The invention relates generally to the field of medicine and morespecifically to machines and processes for monitoring heart instability.

Background Art

Heart rhythm disorders are extremely common in the United States, andcause significant mortality and morbidity. However, there are fewmethods to predict future rhythm disorders (“arrhythmias”) before theyoccur. Instead, physicians rely upon detecting the actual rhythmdisturbance, which precludes early detection and possible prevention ofthese disorders. Many methods currently used also may have seriousside-effects. Over 2 million Americans suffer from Atrial Fibrillation(AF), a rhythm disorder of the atria (top heart chambers) that causesserious symptoms, lost days from work, and potentially death (Chugh,Blackshear et al. 2001). Sadly, AF sometimes is first detected after ithas caused a serious side-effect such as a stroke. Predicting the futuredevelopment of AF in an individual can prevent such catastrophic events.However, clinical practice is so rudimentary in this area that it reliesupon observing episodes of AF to detect future risk, yet many episodesof AF are still missed or misclassified (Chugh, Blackshear et al. 2001).Therefore, prediction of AF has been difficult.

The same problems exist for predicting a variety of important heartinstabilities. Sudden cardiac arrest is the leading cause of mortalityin the U.S., taking over 300,000 lives per year, largely due to therhythm disorders of ventricular tachycardia (VT) or fibrillation (VF)(Myerburg and Castellanos 2006). Current methods of predicting futureVT/VF are inadequate. In fact, risk is often not identified until aftersudden cardiac arrest (SCA) occurs—despite the fact that an individual'schance for surviving out-of-hospital SCA is <10% (Robertson 2000). Theinability to predict future VT/VF also leads to a potential overuse ofpreventive therapy in the form of the implantable cardioverterdefibrillators (Myerburg and Castellanos 2006).

The field is replete with attempts to detect these disorders. Forinstance, it has been shown that human AF, VT and VF, as well as otherless serious heart rhythm disorders, can arise by the mechanism ofreentry (Kuo, Munakata et al. 1983). In this case, barriers (known as“conduction blocks”) can develop and cause otherwise orderly electricalimpulses to disorganize into electrical waves that circulate around thebarrier. In addition, localized regions of scar tissue or ischemictissue propagate electrical activity (depolarization) slower than normaltissue, causing “slow conduction,” which may factor into formation ofthese wavelets. This may lead to errant, circular propagation. Further,“reentry” or “circus motion” may result, which disrupts depolarizationand contraction of the atria or ventricles, and leads to abnormalrhythms (“arrhythmias”). Many tools have been developed to predictrhythm disorders from these large-scale effects, with suboptimalresults.

AF often occurs in patients in whom the atria, the top chamber of theheart, is enlarged or weakened. However, whether the AF is the cause oreffect of the atrial cardiomyopathy (“heart failure of the atrium”) hasbeen unclear.

In one study (Narayan, Bode et al. 2002b), it is shown that actionpotential oscillations during atrial flutter (a different abnormal heartrhythm) may enable the transition to AF. However, action potentialoscillations during atrial flutter occur in only a small minority ofsufferers with AF. In addition, the study observed only the rightatrium. However, the left atrium is critical in the initiation of AF,and is the chamber in which treatment for AF is most effective (Calkinset al., 2007). Action potential fluctuations also arise in the leftatrium near the pulmonary veins, which leads to AF in a large number ofcases (Calkins et al, 2007).

Previous studies from patients with atrial cardiomyopathy undergoingsurgery revealed tissue specimens that showed atrium enlargement,weakening of atrial wall contractions, thickening of atrial walls, andcell loss and destruction in cases of cardiomyopathy of the atrium(Frustaci, Chimenti et al. 1997). Often, but not always, these signs aresecondary to disease of the ventricles. However, testing using tissuespecimens is not a viable clinical tool. Taking tissue from the heart(biopsy) is a risky procedure that may potentially cause seriousside-effects including death. In the atrium, this is almost neverperformed unless a patient is proceeding to surgery for another reason.

Echocardiography can show weakening of contraction and enlargement ofthe atrium. However, this does not specifically indicate any disease. Infact, weakening can be seen in individuals without primary atrialcardiomyopathy or AF, who may have other common and even non-seriousdiseases of the ventricles including left ventricular hypertrophy frommild high blood pressure (Thomas, Levett et al. 2002). Weakening andfibrosis of the wall of the atrium can slow electrical conductionthrough its walls. This leads to a prolonged P-wave duration on thesurface ECG. Many studies have used this measurement to predict AF(Steinberg, Prystowski et al. 1994), but with modest results becausethis factor may not be central to all forms of AF (AF can arise inindividuals without atrial fibrosis or conduction slowing). As a result,these and related measurements are not often used clinically.

Other methods used to assess atrial function include elevated levels ofnatriuretic peptides, yet these methods have not been incorporated intoclinical practice in humans because their predictive value is also poor(Therkelsen, Groenning et al. 2004). Other methods exist to measureatrial size, including magnetic resonance imaging and other techniques,yet these methods do not correlate with atrial function. As a result,these methods are not used in clinical practice.

Methods that have been proposed to predict AF risk, or track propensity,are non-specific and not often used. Common methods include clinicalassociations, such as identifying-individuals with thyroxicosis or heartvalve disease as having increased risk for AF. Further, individuals withventricular disease have higher left atrial pressures which maypredispose them to developing AF. Such ventricular diseases includesimple ones (left ventricular hypertrophy from aging or high bloodpressure), and more complex ones (ventricular cardiomyopathy). Othermethods include identifying a large left atrial size on imaging(echocardiography, MRI). However, none of these clinical associationsaccurately identifies which individuals will develop AF, or when.

Other methods have been described that focus on reentrant mechanisms forAF, but are also not used clinically. Steinberg et al. (Steinberg,Zelenkofske et al. 1993a), Klein et al. (Klein, Evans et al. 1995) andothers showed that prolonged atrial activity indicates slow conductionwhich identifies patients at risk for AF. Work by Narayan et al.(Narayan, Bode et al. 2002b) suggested that the presence of alternatebeat variations (“alternans”) of the timing, shape or amplitude of theP-wave (or an atrial signal surrogate) predicts AF. However, thosestudies pertained only to patients with existing atrial flutter (arelated rhythm disorder) in the right atrium (that is less important forAF), and used pacing for studying some of the patients. Thus, thestudies did not demonstrate results relevant to most patients with AF,who do not have preceding atrial flutter. Other methods includedetecting abnormalities of the sinus node rate that may precede AF(Faddis, Narayan et al. 1999), but also have limited predictive value.

Methods directed to preventing heart rhythm disorders propose fastpacing rates for prevention of arrhythmias, such as overdrive pacing (atfaster rates than observed naturally in the individual). These methodshave had very limited success.

Atrial cardiomyopathy (heart failure of the top chamber of the heart) isa relatively new concept, and is not often diagnosed clinically. As aresult, few therapies have been described to treat atrialcardiomyopathy. However, there is increasing interest in methods ofimproving atrial function.

New evidence suggests that drugs such as angiotensin-receptorantagonists and angiotensin receptor blockers can prevent atrialfibrosis and progression of atrial cardiomyopathy (Wachtell, Lehto etal. 2005). Similar benefits have been shown for beta-receptorantagonists, and also for agents such as HMG co-A reductase inhibitors(Ehrlich, Biliczki et al. 2008). However, these drugs act over years,not acutely, and it is unclear how well they reverse or stabilize atrialcardiomyopathy that has already developed. Further, many of these drugbenefits were discovered indirectly in trials designed to examinebenefits of the drugs on ventricular function. Thus, it is not clear ifthe drugs would lead to similar benefits in direct prospective trials,and in the vast majority of patients with atrial cardiomyopathy withoutventricular cardiomyopathy. Finally, many anti-arrhythmic drugs used toprevent and treat AF are suboptimal (Ehrlich, Biliczki et al. 2008).

Ventricular cardiomyopathy is currently viewed predominantly from astructural perspective. Therefore, an individual's ventricular diseaseis tracked by repeated measurement of left ventricular ejection fraction(LVEF) or the ventricular dimension on an echocardiography. This posesseveral problems. First, the difference between LVEF, for example 30%versus 25%, is of unclear significance in terms of identifying symptoms,treatment, prognosis, or risk for VT/VF. Second, echocardiography orventriculography are only reproducible for LVEF within broad ranges, andother methods such as radionuclide angiography are more cumbersome.Third, clinical practice does not show significance in day-to-day orweek-to-week fluctuations in structural indices.

Current methods to predict the risk for VT/VF are non-specific. Asmentioned above, the most common risk factor is the presence of reducedLVEF or heart failure symptoms. However, these methods over-detect atrisk individuals by a factor of up to 18:1 (i.e. 18 individuals have toreceive prophylactic ICD therapy to save one individual who willactually develop VT/VF) (Myerburg and Castellanos 2006). This methodalso fails to identify over 50% of all individuals who experience SCAand whose LVEF is not reduced. Thus, these criteria are suboptimal.

Rate response of ventricular action potentials at a slow heart rate (109beats per minute—within the range expected for only mild exertion suchas light walking) do not predict VT/VF. (Narayan et al, 2007): This isconsistent with other art (such as U.S. Pat. Nos. 6,915,156 and7,313,437 by Christini and colleagues) which describes methods tocontrol cardiac alternans in action potential duration by controllingthe interval separating beats. These approaches have not translated intothe patient care setting.

Other methods proposed to predict VT/VF have had mixed success. Most ofthese methods focus on presumed reentrant mechanisms, and are indirect.These methods include detecting slow conduction in an ECG (signalaveraged ECG) that may indicate a predisposition to reentry (Cain,Anderson et al. 1996a). Work by Kleiger et al. (Kleiger, Millar et al.1987) shows that reduced 24-hour variability in the interval betweenheart beats (“heart rate variability”) predicts VT or VF. A relatedmethod examines heart rate variability after premature beats (Schmidt,Malik et al. 1999). In a related method, abnormal innervation of theventricle assessed using nuclear imaging may identify risk for VT/VF(Arora, Ferrick et al. 2003b). U.S. Pat. No. 4,802,481 issued to Cohen(Cohen and Smith 1989) and work by others (Smith, Clancy et al. 1988a;Rosenbaum, Jackson et al. 1994; Narayan, Lindsay et al. 1999d) describestechniques for assessing myocardial electrical instability as strictlyalternate-beat fluctuations in T-wave energy (also known as “T-wavealternans”). Newer methods such as described in U.S. Pat. No. 5,555,888issued to Brewer (Brewer and Taghizadeh 1996) and work by Marrouche etal. (Marrouche, Pavia et al. 2002), use alterations in the ventricularactivation after subthreshold current to assess the risk for VT or VF.Finally, abnormal delayed enhancement of the ventricle using magneticresonance imaging may identify risk for VT/VF (Schmidt, Azevedo et al.2007). For VT and VF, success has been suboptimal for tools that probethe reentry circuit with electrophysiologic study (Buxton, Lee et al.2000), the signal-averaged ECG to examine slow conduction (Cain,Anderson et al. 1996a), and indices of repolarization including T-wavealternans (Narayan 2006a) and QT dispersion (Brendorp, Elming et al.2001).

More recent work suggests that nervous activity/innervation can increasethe risk for VT/VF (Stein, Domitrovich et al. 2005) and for AF(Patterson, Po et al. 2005). However, the mechanism linking autonomicactivity with arrhythmias is unclear—particularly in humans. Of note,none of these techniques are part of implantation planning forcardioverter defibrillators (ACC/AHA/ESC 2006) or are used routinely inthe clinic. All have suboptimal predictive value, and therefore tend tobe more of a rough guide to risk rather than a predictive tool.

Several approaches have been described to improve ventricularcardiomyopathy. However, none of these methods work in all patientpopulations, and some failed to reduce VT/VF in tandem with improvementsin heart failure (Bradley 2003b). Some drugs have been shown to improveventricular function in cardiomyopathy. These includeangiotensin-receptor antagonists and angiotensin receptor blockers, andbeta-receptor antagonists (Poole-Wilson, Swedberg et al. 2003). However,these drugs act over years, rather than acutely.

Over the past decade, it has been shown that cardiac resynchronizationtherapy also improves ventricular cardiomyopathy in patients withreduced LVEF, heart failure and evidence for delayed activation betweenventricles (Abraham, Fisher et al. 2002). However, it remains unclearwhether cardiac resynchronization therapy itself improves the aspects ofheart failure that lead to VT/VF, which is why many physicians implantan ICD in tandem with a resynchronization device (ACC/AHA/ESC 2006).Placing a pacing lead close to an arrhythmia circuit enables easiertermination than if leads are remote from that location (Stevenson, Khanet al. 1993; Morton, Sanders et al. 2002a). However, current studiespoorly describe methods of placing a permanent pacemaker ordefibrillator lead to reduce VT/VF. Tse et al. (Xu, Tse et al. 2002),Leclercq et al. (LeClercq, Victor et al. 2000), and Meisel et al.(Meisel, Pfeiffer et al. 2001) among others, show that carefullyselected ventricular pacing—particularly in the left ventricle—canimprove hemodynamics and, based on work by Zagrodzky et al. (Zagrodzky,Ramaswamy et al. 2001), reduce arrhythmia incidence.

Further, it is known that pacing in certain regions of the heart, suchas the right ventricle, can lead to right ventricular cardiomyopathy(DAVID 2002). However, many patients do not experience right ventricularcardiomyopathy due to pacing, and physicians still practice rightventricular pacing. However, the only way to determine if thedetrimental effect is developing is to examine worsening in LVEF.

In animals, AF or atrial cardiomyopathy is not spontaneous but rather iscaused by very rapid pacing or toxic drugs. In animals withexperimentally induced atrial fibrillation, one can see evidence of thechanges in atrial cardiomyopathy from histology or at the sub-cellularlevel (Ausma, van der Velden et al. 2003). However, as described above,obtaining tissue samples from human atria in human being is verydifficult. Human AF is also different from AF in animal models.Therefore, there exists a need to better detect heart instability inhumans.

The following references provide additional background information:Abraham, W. T., et al. (2002), “Cardiac Resynchronization in ChronicHeart Failure”, N Engl J Med. 346: 1845-1853; ACC/AHA/ESC (2006).“ACC/AHA/ESC 2006 Guidelines for Management of Patients With VentricularArrhythmias and the Prevention of Sudden Cardiac Death—ExecutiveSummary. A Report of the American College of Cardiology/American HeartAssociation Task Force and the European Society of Cardiology Committeefor Practice Guidelines (Writing Committee to Develop Guidelines forManagement of Patients With Ventricular Arrhythmias and the Preventionof Sudden Cardiac Death)”, J Am Coll Cardiol 48(5): 1064-1108; Arora,R., et al. (2003b), “I-123 MIBG imaging and heart rate variabilityanalysis to predict the need for an implantable cardioverterdefibrillator”, Journal of Nuclear Cardiology 10(2): 121-131; Ausma, J.,et al. (2003), “Reverse Structural and Gap-Junctional Remodeling AfterProlonged Atrial Fibrillation in the Goat”, Circulation 107(15):2051-2058; Bloomfield, D. M., et al. (2002). “Interpretation andClassification of Microvolt T-Wave Alternans Tests”, J. CardiovascElectrophysiol. 13(5): 502-512; Bradley, D. J. (2003b), “CombiningResynchronization and Defibrillation Therapies for Heart Failure”, JAMA289(20): 2719; Brendorp, B., et al. (2001), “QT Dispersion Has NoPrognostic Information for Patients With Advanced Congestive HeartFailure and Reduced Left Ventricular Systolic Function”, Circulation103: 831-5; Brewer, J. E. and E. Taghizadeh, U.S. Pat. No. 5,555,888,“Method for automatic, adaptive, active facilitation to accessmyocardial electrical instability”; Bristow, M. R., et al. (2004),“Cardiac-Resynchronization Therapy with or without an ImplantableDefibrillator in Advanced Chronic Heart Failure”, N Engl J Med 350(21):2140-2150; Buxton, A. E., et al. (2000), “Electrophysiologic testing toidentify patients with coronary artery disease who are at risk forsudden death. Multicenter Unsustained Tachycardia Trial Investigators(MUSTT).” N Engl J Med. 342(26): 1937-45; Cain, M. E., et al. (1996a),“Signal-Averaged Electrocardiography: ACC Consensus Document”, J. Am.Coll. Cardiol. 27(1): 238-49; Calkins H., et al. (2007), Heart Rhythm4:816-861; Chugh, S. S., et al. (2001), “Epidemiology and naturalhistory of atrial fibrillation: clinical implications.” J Am CollCardiol 37(2): 371-8; Cohen, R. J. and J. M. Smith U.S. Pat. No.4,802,491 (1989), “Method and apparatus for assessing myocardialelectrical instability”; David, D. T. I. (2002), “Dual-Chamber Pacing orVentricular Backup Pacing in Patients With an Implantable Defibrillator:The Dual Chamber and VVI Implantable Defibrillator (DAVID) Trial”, J AmMedical Association 288(No. 24): 3115-3123; Ehrlich, J. R., et al.(2008), “Atrial-selective approaches for the treatment of atrialfibrillation.” J Am Coll Cardiol 51(8): 787-92; Faddis, M. N., et al.(1999), “A Decrease in Approximate Entropy Predicts The Onset of AtrialFibrillation” [abstract], Pacing and Clinical Electrophysiology 22(4(part II)): 358; Franz, M. R., et al. (1988a). “Cycle length dependenceof human action potential duration in vivo. Effects of singleextrastimuli, sudden sustained rate acceleration and deceleration, anddifferent steady-state frequencies”, J Clin Invest 82(3): 972-979;Frustaci, A., et al. (1997), “Histological Substrate of Atrial Biopsiesin Patients With Lone Atrial Fibrillation.” Circulation 96(4):1180-1184; Gold, M. R., et al. (2000a). “A Comparison of T WaveAlternans, Signal Averaged Electrocardiography and ProgrammedVentricular Stimulation for Arrhythmia Risk Stratification.” J. Am.Coll. Cardiol. 36: 2247-2253; Gong et al. (2007) Circulation 115:2092-2102. [0050] Hao, S., D. Christini, et al. (2004), “Effect ofbeta-adrenergic blockade on dynamic electrical restitution in vivo”, AmJ Physiol Heart Circ Physiol 287(1): H390-4; Kalb, S., et al. (2004).“The restitution portrait: a new method for investigating rate-dependentrestitution”, J Cardiovasc Electrophysiol 15(6): 698-709. Kleiger, R.E., P. Millar, et al. (1987). “Decreased heart rate variability and itsassociation with increased mortality after acute myocardial infarction”,Am. J. Cardiol. 59: 256-262. [0053] Klein, M., S. J. Evans, et al.(1995). “Use of P-wave triggered, P-wave signal-averagedelectrocardiogram to predict atrial fibrillation after coronary bypasssurgery.” Am. Heart J. 129(5): 895-901; Kuo, C.-S., et al. (1983),“Characteristics and possible mechanism of ventricular arrhythmiadependent on the dispersion of action potential durations”, Circulation67: 1356-1367; Laurita, K. R. and D. S. Rosenbaum (2008), “Mechanismsand potential therapeutic targets for ventricular arrhythmias associatedwith impaired cardiac calcium cycling.” J Mol Cell Cardiol 44(1): 31-43;LeClercq, C., F. Victor, et al. (2000), “Comparative effects ofpermanent biventricular pacing for refractory heart failure in patientswith stable sinus rhythm or chronic atrial fibrillation”, The AmericanJournal of Cardiology 85(9): 1154-1156; Marrouche, et al. (2002),“Nonexcitatory stimulus delivery improves left ventricular function inhearts with left bundle branch block”, J Cardiovasc Electrophysiol13(7): 691-5; Meisel, E., et al. (2001), “Investigation of coronaryvenous anatomy by retrograde venography in patients with malignantventricular tachycardia”, Circulation 104(4): 442-447; Morton, J. B., etal. (2002a), “Sensitivity and specificity of concealed entrainment forthe identification of a critical isthmus in the atrium: relationship torate, anatomic location and antidromic penetration”, J Am Coll Cardiol39(5): 896-906; Myerburg, R. J. and A. Castellanos (2006), “Emergingparadigms of the epidemiology and demographics of sudden cardiacarrest”, Heart Rhythm 3(2): 235-239; Narayan, S. M. (2006a), “T-WaveAlternans and The Susceptibility to Ventricular Arrhythmias: State ofthe Art Paper”, J Am Coll Cardiol 47(2): 269-281; Narayan, S. M., et al.(2002b), “Alternans Of Atrial Action Potentials As A Precursor Of AtrialFibrillation”, Circulation 106: 1968-1973; Narayan, S. M., et al.,“T-wave alternans, Restitution of Ventricular action potential durationand outcome”, J Am Coll Cardiol 2007; 50: 2385-2392; Narayan, S. M., etal. (1999d), “Demonstrating the Pro-arrhythmic Preconditioning of SinglePremature Extrastimuli Using the Magnitude, Phase and TemporalDistribution of Repolarization Alternans”, Circulation 100: 1887-1893;Narayan, S. M. and J. M. Smith (1999b), “Spectral Analysis of PeriodicFluctuations in ECG Repolarization”, IEEE Transactions in BiomedicalEngineering 46(2): 203-212; Narayan, S. M. and J. M. Smith (2000c),“Exploiting Rate Hysteresis in Repolarization Alternans to Optimize theSensitivity and Specificity for Ventricular Tachycardia”, J. Am. Coll.Cardiol. 35(5): 1485-1492; Patterson, E., et al. (2005). “Triggeredfiring in pulmonary veins initiated by in vitro autonomic nervestimulation”, Heart Rhythm 2(6): 624-31; Poole-Wilson, P. A., K.Swedberg, et al. (2003), “Comparison of carvedilol and metoprolol onclinical outcomes in patients with chronic heart failure in theCarvedilol Or Metoprolol European Trial (COMET): randomised controlledtrial”, The Lancet 362(9377): 7-13; Robertson, R. M. (2000, “SuddenDeath from Cardiac Arrest—Improving the Odds”, N Engl J Med 343(17):1259-1260; Rosenbaum, D. S., et al. (1994), “Electrical alternans andvulnerability to ventricular arrhythmias”, N Engl J Med 330(4): 235-41;Schauerte, P., et al. (2000a), “Transvenous Parasympathetic Nervestimulation in the Inferior Vena Cava and Atrioventricular Conduction”,J. Cardiovascular Electrophysiol. 11(1): 64-69; Scherlag, B. J., et al.(2005), “Electrical Stimulation to Identify Neural Elements on theHeart: Their Role in Atrial Fibrillation”, Journal of InterventionalCardiac Electrophysiology 13(0): 37-42; Schmidt, A., et al. (2007),“Infarct tissue heterogeneity by magnetic resonance imaging identifiesenhanced cardiac arrhythmia susceptibility in patients with leftventricular dysfunction”, Circulation 115(15): 2006-14; Schmidt, G., etal. (1999), “Heart-rate turbulence after ventricular premature beats asa predictor of mortality after acute myocardial infarction”, Lancet 353:1390-1396; Smith, J. M., et al. (1988a), “Electrical Alternans andcardiac electrical instability”, Circulation 77(1): 110-21; Stambler, B.S. and K. A. Ellenbogen (1996b), “Elucidating the Mechanisms of AtrialFlutter Cycle Length Variability Using Power Spectral AnalysisTechniques”, Circulation 94(10): 2515-2525; Stein, P. K., et al. (2005),“Traditional and Nonlinear Heart Rate Variability Are Each IndependentlyAssociated with Mortality after Myocardial Infarction”, Journal ofCardiovascular Electrophysiology 16(1): 13-20; Steinberg, J. S., et al.(1994), “Use of the signal-averaged electrocardiogram for predictinginducible ventricular tachycardia in patients with unexplained syncope.Relationship to clinical variables in a multivariate analysis”, J. Am.Coll. Card. 23: 99; Steinberg, J. S., et al. (1993a), “The value of theP-wave signal-averaged electrocardiogram for predicting atrialfibrillation after cardiac surgery”, Circulation 88:2618; Stevenson, W.G., et al. (1993), “Identification of reentry circuit sites duringcatheter mapping and radiofrequency ablation of ventricular tachycardialate after myocardial infarction”, Circulation 88: 1647-1670;Therkelsen, S., et al. (2004), “Atrial Volume and ANP in PersistentAtrial Fibrillation—Before and After Cardioversion” (abstract),Circulation 110(17 Suppl); Thomas, L., et al. (2002), “Compensatorychanges in atrial volumes with normal aging: is atrial enlargementinevitable?”, Journal of the American College of Cardiology 40(9):1630-1635; Wachtell, K., M. Lehto, et al. (2005), “Angiotensin IIreceptor blockade reduces new-onset atrial fibrillation and subsequentstroke compared to atenolol: The Losartan Intervention For End pointreduction in hypertension (LIFE) study”, Journal of the American Collegeof Cardiology 45(5): 712-719; Walker, et al. (2003), “Hysteresis EffectImplicates Calcium Cycling as a Mechanism of Repolarization Alternans”,Circulation 108(21): 2704-2709; Watanabe, K., et al. (1980), “ComputerAnalysis of the Exercise ECG: A Review”, Prog. Cardiovasc. Dis. 22(6):423-446; Weiss, J. N., et al. (2006). “From Pulsus to Pulseless: TheSaga of Cardiac Alternans (Review)”, Circ Res 98: 1244; Xu, W., et al.(2002), “New Bayesian Discriminator for Detection of AtrialTachyarrhythmias”, Circulation 105: 1472-1479; Zagrodzky, J. D., et al.(2001), “Biventricular pacing decreases the inducibility of ventriculartachycardia in patients with ischemic cardiomyopathy”, Am J Cardiol87(10): 1208-1210.

SUMMARY OF THE INVENTION

In one embodiment, a method of diagnosing the onset of heart instabilitycomprises monitoring signals from a beating human heart, detecting oneor a combination of oscillations in the signal shape, oscillations inthe signal duration or changes in signal characteristics in response tochanges in rate or beat irregularity; and assigning a risk for heartinstability based at least in part on the detecting.

In another embodiment, a method of treating the potential onset of heartinstability comprises monitoring signals from a beating human heart,detecting one or a combination of oscillations in the signal shape,oscillations in the signal duration or changes in signal characteristicsin response to changes in rate or beat irregularity, modifying tissuestructure and/or function, detecting attenuated oscillations and/orchanges in signal characteristics in response changes in rate and/orbeat irregularity.

In another embodiment, an apparatus for diagnosing and treating heartinstability comprises one or more sensors configured to detect signalsfrom a beating human heart, means for detecting one or a combination ofoscillations in the signal shape, oscillations in the signal duration orchanges in signal characteristics in response to changes in rate or beatirregularity, means for assigning a risk for heart rhythm irregularitiesbased at least in part on the detecting, and means for modifying tissuestructure and/or function based at least in part on the detecting.

In another embodiment, an apparatus for creating a risk assessment forheart rhythm irregularities comprises at least one sensor, an analyticengine comprising a module configured to measure a signal received viathe at least one sensor, a module configured to measure a rate-responseof the signal at a plurality of rates, and a module configured toproduce and/or change a determined risk for heart rhythm irregularitiesbased at least in part on the rate-response measurement.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1A-1C illustrate monophasic heart action potential signals thatmay be analyzed according to certain embodiments.

FIGS. 2A-2B illustrate one embodiment of analyzing human ventricularaction potential signals to compute a rate-behavior (restitution) curve.

FIG. 2C illustrates the direct relationship between steep atrialrate-behavior (restitution) of action potentials, and immediate onset ofatrial fibrillation in a human subject.

FIGS. 3A-3B illustrate one embodiment of analyzing human atrial signalsto compute a rate-behavior (restitution) curve;

FIG. 4 illustrates one embodiment of analyzing human heart signals fordynamic (rate-related) conduction slowing, when beats are early (andhave short diastolic intervals);

FIGS. 5A and 5B further illustrate rate-related conduction slowing,again most prominent for early beats (short diastolic intervals).

FIG. 6 illustrates fluctuations/oscillations in human ventricularsignals (action potentials) in a human subject who later developedventricular arrhythmias after many months.

FIGS. 7A-7C illustrate rate-related fluctuations/oscillations in humanatrial signals (action potentials) in a human subject who was largelywithout symptoms but who subsequently developed atrial fibrillation.

FIG. 8 illustrates one embodiment of a method of treatment of alteringactivation sequence to allow cellular metabolic components to regainequilibrium.

FIG. 9 illustrates embodiments of major electrical (pacing) modes oftreatment.

FIG. 10 illustrates out-of-phase pacing modes of treatment, to attenuateoscillations that may lead to disease.

FIG. 11 illustrates one embodiment of a system for analyzing andtreating heart instability.

FIGS. 12A and 12B are a flowchart of one embodiment of a method ofdetermining a risk level of a patient for developing heart instabilityand determining efficacy of applied treatment protocols.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Detailed descriptions of certain embodiments are provided herein. It isto be understood, however, that the present invention may be embodied invarious forms. Therefore, specific details disclosed herein are not tobe interpreted as limiting, but rather as a basis for the claims and asa representative basis for teaching one skilled in the art to employ theembodiments in virtually any appropriately detailed system, structure ormanner. Before moving on to specific details, certain aspects of certainembodiments of the invention are presented below.

Embodiments of methods and systems for treating heart or possibly otherorgan instability are disclosed herein. In accordance with oneembodiment, there is disclosed a method to create an index of health anddisease computed from fluctuations in a biologic signal. The index ofhealth or disease may pertain to components of an organ. The signal maycomprise many types.

Certain embodiments comprise an apparatus to measure the biologicalsignal with sufficient fidelity to detect small variations. Theapparatus may perturb the biological signal. The apparatus may attenuatethe index of disease and thus improve health.

Certain embodiments may include a method to create an index of healthand disease computed from fluctuations in a biologic signal includingone or more of the following: wherein said index measures theperiodicity, amplitude and phase of oscillations in the signal overtime; wherein said oscillations are repetitive; wherein saidoscillations do not repeat; wherein said signal oscillations vary aftera perturbation; wherein the perturbation is altered activation rate orsequence; wherein the perturbation is altered activation of theautonomic nervous system; wherein the perturbation is modification oftissue structure; wherein the perturbation is modification of tissuefunction (behavior); wherein said signal oscillations measurebiochemical changes in the organ cells responsible for disease; whereinsaid biochemical changes are fluctuations in calcium; wherein saidbiochemical changes are fluctuations in potassium; wherein saidbiochemical changes are fluctuations in metabolic components; whereinsaid index measures variations of said signal between regions of theorgan or within the body; wherein variations over time are measured fromactivations of components of an organ; and wherein the organ is theheart.

In certain embodiments, the index of health or disease pertains tocomponents of an organ. Some of the embodiments may include one or moreof the following: wherein said index of health or disease pertains tocomponents of an organ; wherein the organ is the heart; wherein thecomponents are the atria; wherein the components are the ventricles;wherein disease refers to cardiomyopathy; wherein disease refers torhythm disorders; wherein disease refers to coronary artery disease;wherein rhythm disorders include atrial tachycardia, atrialfibrillation, ventricular tachycardia or ventricular fibrillation;wherein disease refers to medication side-effects; wherein health refersto absence of cardiomyopathy; wherein health refers to absence of heartrhythm disorders; wherein health refers to improved autonomic nervoussystem regulation; wherein health refers to improved hormonalregulation; wherein health refers to effectiveness of therapy; whereintherapy is cardiac device therapy; wherein therapy is transplantation;wherein the organ is smooth muscle, such as in the gastrointestinal orrespiratory systems; wherein the organ is skeletal muscle; wherein theorgan is the brain.

In certain embodiments, the biological signal comprises one or more ofthe following: an intracellular or extracellular action potentialwherein variations pertain to the shape, wherein variations pertain tothe duration, and/or wherein variations pertain to the amplitude; amonophasic action potential; an organ electrogram, wherein variationspertain to activation, wherein variations pertain to repolarization,and/or wherein variations pertain to diastole; an electrocardiogramwherein variations pertain to the QRS and T-waves, wherein variationspertain to the QT interval, wherein variations pertain to the STsegment, and/or wherein variations pertain to the TP segment; amagnetocardiogram wherein variations pertain to the QRS and T-waves,wherein variations pertain to the QT interval, represents conductiontime between regions of the organ, and/or wherein conduction slows forcertain perturbations; a measure of tissue motion, wherein variationsmeasure wall motion on echocardiography, wherein variations measure wallmotion on tissue Doppler imaging, and/or wherein variations pertain tothe echocardiographic displacement of the atrioventricular ring.

In certain embodiments, the biological signal indicates one or more ofthe following: functioning of the central nervous system; functioning ofthe respiratory system; functioning of the urogenital system;functioning of the gastrointestinal system; functioning of smoothmuscle; functioning of skeletal muscle.

Other embodiments may include an apparatus to measure the biologicalsignal with sufficient fidelity to detect small variations, theapparatus comprising: a sensor; noise-reduction and filtering apparatus;means of transmitting said signal using physical media, as electricalsignals along wires or within body fluid; means of transmitting saidsignal wirelessly; an apparatus to analyze the signal to construct saidindex; an apparatus to communicate the said index to the health careprovider and patient; wherein the sensor is in contact with the organ;wherein the sensor is a pacing lead; wherein the sensor is elsewhere inthe body but not in contact with the organ; wherein the sensor does notcontact the body and remotely measures said signal.

Certain embodiments may include an apparatus to perturb said biologicalsignal comprising: means for altering the rate of activation of theorgan; means for altering the sequence (regularity or irregularity) ofactivation of the organ; means for altering autonomic nervous control ofthe organ; wherein the autonomic nervous control is of the atria of theheart; wherein the autonomic nervous control is of the ventricles of theheart; means for altering hormonal influences of the organ; and meansfor altering biochemical equilibrium in the organ.

Certain embodiments may include an apparatus to attenuate said index ofdisease, prevent onset of the heart instability and thus improve health,the apparatus including one or more of the following: means forincreasing or decreasing activation rate if said index is found to bedependent upon heart rate; means for altering the pattern of activationof the organ, based on said index; wherein the new pattern enablescellular processes to normalize; wherein the new pattern includes slowactivations for cellular processes to regain equilibrium; wherein thenew pattern includes fast activations to achieve a desired average heartrate; wherein the new pattern attenuates disease-forming oscillations toprevent the disease onset; wherein the new pattern is non-regular;wherein the new pattern is regular; means for modifying tissuestructure, such as by ablation; means for modifying tissue function,such as by using an external electrical field; means for modifyingautonomic nervous regulation of the organ by altering the structure orfunction or nerves; wherein the nerves alter functioning in the heartatria; wherein the nerves alter functioning in the heart ventricles;wherein the nerves alter functioning in the gastrointestinal tract;wherein the nerves are in the central nervous system; wherein the nervesalter functioning in the urogenitary system; wherein the nerves alterfunctioning in the respiratory system.

Biological systems require many checks and balances to ensure stabilityand health of the individual. These checks and balances are implementedby complex regulatory systems. Although many potentially seriousdiseases arise if these systems fail, few methods exist in humans todetect impending failure of these systems to diagnose disease at anearly stage, to guide therapy, or to track its effectiveness. Systemsand methods are described herein that may detect impending failure ofsuch regulatory systems and, in the process, predict and preventdisease.

Without being bound to any particular theory of operation, the inventorhas recognized that AF and VT/VF may start from subtle cellularmetabolic abnormalities, rather than clear structural disease.Fundamentally, the failure of the arrhythmia prediction tools utilizedto date may stem from their inability to accurately or consistentlydetect these abnormalities. Thus, in some embodiments, it is postulatedthat subtle abnormalities in cell functioning can be uncovered todiagnose disease at an early stage. Methods and systems of treatment aredescribed which attenuate abnormalities and prevent the onset and theprogression of disease (e.g. in the atrium and/or the ventricle),including the onset of heart rhythm instability.

As described herein, the inventor has found that AF is linked withoscillations and abnormal rate-behavior of atrial action potentials inhumans. The inventor has also linked VT/VF with oscillations andabnormal rate-behavior of ventricular action potentials in humans. Thesesignals can indicate impending failure of cell-functions such asregulation of intracellular calcium, which is linked to the eventualfailure of mechanical function (cardiomyopathy) and electrical function(heart rhythm disorders). Certain embodiments thus detect risk at anearlier stage and also guide effective therapy to stabilize suchoscillations and prevent disease.

It has also been found by the inventor that nervous activationpredisposes humans to arrhythmias (atrial and ventricular) by alteringcell-level mechanisms which cause fluctuations or abnormal rate-behaviorof action potentials. Thus, certain embodiments are based on theinventor's recognition that these fluctuations represent the abnormalcellular handling of calcium or other biochemicals. The abnormalcellular handling of calcium or other biochemicals has been seen toindicate arrhythmias in canines and other animal studies (Patterson, Poet al. 2005), but has not previously been shown in humans. Over time,this imbalance may worsen which explains progressive cardiomyopathy(increasing atrial size) and an increasing risk for AF. Thus, certainembodiments described herein detect failure in the cellular regulationof calcium, and thus potential atrial cardiomyopathy (heart failure), inthe beating heart of patients by tracking the underlying failure ofregulation mechanisms.

Abnormal cellular calcium regulation due to ventricular cardiomyopathymay also explain VT or VF onset. Certain embodiments, therefore, measurefluctuations in heart electrical (or mechanical) signals to probe thefailure of regulatory mechanisms underlying VT/VF. In certainembodiments, treatment can also be provided to attenuate fluctuationsand suppress VT/VF, and certain such embodiments can then track theeffectiveness of this and other treatments.

The physiological basis of the detection methods described herein can bedescribed with reference to FIGS. 1A-7C which provide example signalsand signal characteristics that can be analyzed in some embodiments ofthe invention. As described further below, a wide variety of signaltypes can be analyzed in accordance with the principles of theinvention, and the specific embodiments below are merely exemplary.

FIG. 1A illustrates a series of human action potential (AP or MAP)signals 105 recorded from a location in a human ventricle. FIG. 1B showstwo individual action potentials recorded from a location in the leftatrium 110 and a location in the left ventricle 120 respectively. Eachaction potential has phases 0, 1, 2, 3 and 4. Phases 0-1 indicatedepolarization and phases 2-3 indicate repolarization. Phase 4 indicatesthe time interval from one beat to the next. In certain embodiments, therate response (restitution) of one or more components may be determined,focusing on rate-response of AP duration (time from phase 0-3) and APphase 2 amplitude.

FIG. 1C illustrates various signal characteristics of action potentialsthat can be analyzed to indicate cellular events and thus predictpropensity for AF and VT/VF. For example, for action potentials, thetime taken from onset of the action potential (phase 0) to the time of90% repolarization from the plateau phase (near the end of phase 3)designated 130 in FIG. 1C is termed APD90. The diastolic interval 140 isthe time from the APD point of the prior beat to the initiation of thebeat in question. In various embodiments of the invention, anyrepolarization phase can be used (for instance, APD70, APD80), and sothe term APD will be used herein to refer to any and all of theserepolarization phases. In addition, certain surrogates may be used tomeasure repolarization phases. Some surrogates may better correlate withcertain repolarization phases; in particular, unipolar electrogramactivation recovery intervals correlate well with APD90 (Yue, Paisey andcoworkers, Circulation 2004). Rate response characteristics of the APbiosignal and their relationship to arrhythmias are illustrated in FIGS.2A-7C.

FIGS. 2A and 2B illustrate one embodiment of analyzing human ventricularsignals to compute a rate-behavior (restitution) curve. FIGS. 2A and 2Billustrate ventricular action potentials, although analysis may beanalogous for any biosignal. In both FIGS. 2A and 2B, the top panels 210and 220 respectively each illustrate for two different subjects a seriesof regular beats of the heart then an early beat. These panels thusillustrate a beat irregularity in that the interval between beats is notconstant over the time of observation. To analyze these signals, actionpotentials are separated, and each measured to determine diastolicinterval and APD as shown in FIG. 1C.

Bottom panels 230 and 250 respectively illustrate the rate response(restitution) curves for APD for these two subjects. The rate-behavior(restitution) curve is traditionally plotted as the APD (vertically)against the preceding diastolic interval separating one beat from thenext (shown in item 140). The illustrated restitution curve is createdfrom early beats (as in panels 210, 220), but can also be created duringany rate variations, such as variations in heart rate between rest(slow), minimal exertion (moderate range rate) and maximal exertion(fastest rate).

The APD restitution curve is conventionally described by severalparameters, including its maximum slope (illustrated in items 230, 250),range between minimum and maximum APD and the longest diastolic intervalfor which slope is greater than 1. It has been shown in animals, butnever before in humans, that maximum APD restitution slope >1 predictsspontaneous arrhythmias. For example, the individual associated with thedata in FIG. 2A had maximum APD restitution slope <1, and did notexperience arrhythmias on follow-up. The individual associated with FIG.2B had APD restitution slope >1 and did experience arrhythmias. Notably,restitution (rate response) can be measured for any signal component,such as upstroke velocity (phase I, sodium channel functioning), plateauvoltage (phase II, sodium, calcium and other channel functioning),duration (phase III/IV, calcium and potassium channel function), andfluctuations in diastole (between action potentials) that may indicatedisequilibrium in a variety of cell components.

FIG. 2C illustrates atrial action potential restitution from a humanpatient with minimal structural disease who has paroxysmal AF. Thisshows the direct relationship between steep atrial rate-behavior(restitution) of action potentials, and immediate onset of atrialfibrillation in a human subject. Panel 260 shows that a single prematurebeat (S2) initiates AF (beats labeled F1-F7) in the patient. The APD foreach beat, and its preceding diastolic interval, is shown. Notably,extreme APD oscillations are seen leading to wave break and AF. Panel270 illustrates how steep APD restitution in this patient results fromextreme oscillations from beat S1 to S2 to F1, F2, F3 and so on in AF.This further validates the importance of steep atrial restitution incausing human AF.

FIGS. 3A and 3B illustrate another example of analyzing human atrialsignals to compute a rate-behavior (restitution) curve. Top panels 310and 320 show a series of atrial beats then an early beat. The signalillustrated in FIG. 3A was obtained from a subject that had no AFhistory. The signal illustrated in FIG. 3B was obtained from a subjectthat had paroxysmal AF. For both signals, action potentials are measuredand parsed as before (FIG. 1C, FIGS. 2A-2C).

Lower panels 330 and 350 show the rate response (restitution) curves forAPD for these subjects. Again, the rate-behavior (restitution) curve isplotted as the APD (vertically) against the diastolic intervalseparating one beat from the next. As above, the illustrated restitutioncurve is created from early beats (as in panels 310 and 320), but canalso be created from beat-to-beat variations between rest (slow rates),minimal exertion (moderate rates) and maximal exertion (fastest rates).The restitution curve is described by several parameters, including itsmaximum slope, the range between maximum and minimum APD, and thelongest diastolic interval for which slope is greater than 1.

The individual in FIG. 3A is a control subject with no AF, whose maximumAPD restitution slope <1. The individual in FIG. 3B did experience AFand had APD restitution slope >1.

Interestingly, FIG. 4 shows an individual with longstanding AF andconduction slowing on extra beats. This delayed the actual timing of theearly beat, thus truncating the left portion of the APD restitutioncurve and producing slope <1. This may be the reason that it has neverbefore been shown in human atria that APD restitution slope >1identifies patients who will develop AF. Because conduction slowing insubjects with longstanding AF has masked recognition of the correlation,the inventor is the first to utilize this relationship in humandiagnosis and treatment.

The effect of conduction slowing is illustrated further in FIGS. 5A and5B. FIGS. 5A and 5B illustrate one embodiment of analyzing human heartsignals for dynamic (rate-related) conduction slowing. The effect ofconduction slowing on interpretation of results obtained fromrestitution analysis is described with respect to this Figure. FIG. 5Ashows that conduction slowing (prolonged activation time) in subjectswith early stage (paroxysmal) AF occurs only for very early beats (withvery short diastolic intervals). It is relatively difficult to uncoversuch slowing. This is similar to predictions from computer models (Gong,et al. 2007), but has never previously been observed in the atria ofhumans. Conversely, FIG. 5B shows that, in a subject with longstandingadvanced AF, conduction slows even for less-early beats (or relativelyslow rates, with long diastolic intervals). In other words, conductionslowing is observed more easily. Accordingly, conduction slowingexplains observed APD restitution flattening in patients with persistentAF (atrial cardiomyopathy)—in which conduction delay for the earliestbeats truncates the leftmost portion of the APD rate-behavior(restitution) curve.

In addition to APD rate behavior, the inventor has also found that beatto beat fluctuations or oscillations in MAP wave shape (such as phase IIamplitudes) can also be predictive of arrhythmias.

FIG. 6 illustrates fluctuations in human ventricular signals (actionpotentials) in a human subject who later developed ventriculararrhythmias after many months. These examples of fluctuations in humanaction potentials mechanistically precede and thus predict arrhythmias.As described below, baseline corrected biosignals may be used first.

In FIG. 6, panel 610 shows ventricular action potentials in a humansubject who went on to develop potentially lethal ventriculararrhythmias. In panel 620, these action potentials were aligned asdescribed below. As can be seen, the even beats (coded blue) and oddbeats (coded red) segregate based on shape. In other words, there arebeat-to-beat fluctuations that alternate in this case (alternans; otherpatients may demonstrate fluctuations on a third-beat basis or with someother periodicity). Although this can be quantified visually, spectraldecomposition as illustrated by use of a fast Fourier transform (FFT)provides reproducible quantification.

Panel 630 of FIG. 6 illustrates a spectral analysis of the actionpotentials of panel 610. In this case, 64 contiguous APs were selected,baseline corrected to the mean of a 10 ms segment starting 20 ms priorto phase 0 maximum dV/dt, and aligned to their upstroke (phases 0-1)(Narayan and Smith 1999b). Successive APs were represented as 2-Dmatrices R (n, t), where n indicates beat number (0≤n≤63), and t thetime sample (Narayan and Smith 1999b). A Fast Fourier Transform (FFT)was used to compute power spectra across beats (arrow-wise in FIG. 1C)for each t, and then spectra were summated for portions of the AP.Spectral AP fluctuations magnitude was represented by the dimensionlessk-score:

$\frac{{\sum T} - \mu_{noise}}{\sigma_{noise}},$

where ΣT is spectral magnitude at 0.5 cycles/beat, and μ_(noise) andσ_(noise) are the mean and SD of noise, respectively. The noise windowwas selected adjacent to alternans frequency (0.33-0.49 Hz) to avoid the0.125-0.25 Hz respiratory peak (Narayan and Smith 1999b). A k>0indicates that the magnitude of fluctuations (which may representalternans) exceeds noise (Bloomfield, Hohnloser et al. 2002). The meanvoltage of alternation V_(alt) across the AP duration (also referencedto the noise floor) was estimated as:

$\sqrt{\frac{{\sum T} - \mu_{noise}}{{AP}\mspace{14mu} {duration}}}\mspace{14mu} {( {{in}\mspace{14mu} {µV}} ).}$

Panel 630 shows the resulting frequency spectrum, where alternate-beatfluctuations result in a peak at a frequency of half-the-heart-beat. Theinventor has further noted that the above described action potentialfluctuations correlate strongly with T-wave alternans on the surfaceECG. As stated above, this patient developed serious ventriculararrhythmias some months later. Data from Narayan and Smith (Narayan andSmith 2000c) and Walker and Rosenbaum (Walker, Wan et al. 2003) providestrong evidence that such fluctuations represent calcium fluctuations.

FIGS. 7A-7C illustrate fluctuations in human atrial signals (actionpotentials) in a human subject who was largely without symptoms but whosubsequently developed atrial fibrillation. With this subject,oscillations in atrial signals are seen which increase withprogressively faster pacing prior to the onset of AF. Panel 710 of FIG.7A shows atrial action potentials during slow pacing. Panel 720 of FIG.7A shows the aligned (red/blue) beats superimposed as described above.Minimal fluctuations are visible, although spectral analysis (FIG. 7A,panel 730) reveals small fluctuations (in this case, alternatingfluctuations).

FIG. 7B illustrates signals from the same subject at faster heart rates.At faster rates (panel 740), fluctuations are visible (indicated asShort (S), Long (L), but also seen in dome amplitude). In panel 750,aligned beats are separated into red and blue groups, and spectralanalysis (FIG. 7B, panel 760) reveals marked oscillations.

As shown in FIG. 7C, fluctuations can be dramatic prior to AF onset(panel 770). Panel 780 shows that this patient had APD restitutionslope >1 (although this is not seen in all patients). Notably, somepatients may have electrical fluctuations at slow rates, which mayrepresent cellular calcium abnormalities.

The relationship of disease risk to rate-response (restitution) iscomplex. In the absence of significant structural disease, a restitutionslope >1 may cause signal oscillations and predict/cause disease.However, in the presence of cellular and/or structural disease,mechanisms such as conduction slowing are involved in the initiation andmaintenance of disease and add complexity to the relationship withrestitution slope. These factors are summarized below in a risk scoretable for atrial fibrillation; similar risk score tables can beconstructed for ventricular rhythm disorders with analogous elements.

RISK SCORE TABLE FOR ATRIAL FIBRILLATION Structural APD Rest FluctuationDisease? Slope (Alternans) CV Slowing? Diagnosis Risk No <1 No Only forv. Minimal atrial Low early beats disease No <1 Yes (at fast Only for v.Minimal atrial Low rates only) early beats disease No >1 Yes (at fastOnly for v. Paroxysmal AF Medium. AF at rates) early beats (earlydisease) fast rates Yes <1 No Yes Consistent with Low to medium. AgingYes <1 Yes (at slow Yes Persistent AF High or fast rates) Yes >1 Yes (atmany No Persistent AF with High rates) high adrenergic tone

The embodiments of risk score assessment described herein differ fromcurrent methods. For example, the observation that alternans of atrialintracardiac signals predict the onset of AF (Narayan, Bode et al.2002b) has only been shown in the right atrium and only in patients withpre-existing atrial flutter whose rhythms transitioned to AF. Systemsand method described herein may measure fluctuations which representcellular abnormalities, and may be detected at slow rates. Thesemeasured fluctuations may better predict arrhythmia initiation thanshown in studies by others (Narayan, Bode et al. Circulation 2002b).

These principles allow for an unparalleled method for identifying AFrisk. In some embodiments, these methods use human electrical signalsthat directly measure cellular pathophysiology, rather than associations(such as age, left atrial diameter and so on (Chugh, Blackshear, J AmColl Cardiol. 2001). As described, the input signals may be monophasicaction potentials, but can be approximated from other clinical signalssuch as unipolar or bipolar signals with sufficient contact pressurewith the heart chamber. In addition, signals can be derived from anyclinical electrode, catheter, or pacemaker lead.

The embodiments described herein are different from frequency analysisof electrograms, such as shown in work by Stambler et al. (Stambler andEllenbogen 1996b). Those authors used FFT to analyze frequencycomponents constituting the entire waveform. Conversely, embodiments ofmethods described herein use FFT to document beat-to-beat variability ona second beat, third beat (and so on) basis. Much of the currentstudies, such as U.S. Pat. No. 6,064,906 issued to Langberg, and U.S.Pat. No. 6,178,347 issued to Olsson, also do not disclose use of FFT inthis fashion. Furthermore, the embodiments described herein may examinebeat-to-beat fluctuations and thus exclude the confounding effects ofsub-harmonic and harmonic frequencies.

Certain embodiments additionally provide a novel means of detecting thebasis for T-wave fluctuations, and more specifically T-wave alternans(an ECG tool to predict lethal ventricular arrhythmias described in U.S.Pat. No. 4,802,491 issued to Cohen and U.S. Pat. No. 5,148,812 issued toVerrier). The presence of T-wave alternans predicts the presence of VTsubstrates in several studies by Gold et al. (Gold, Bloomfield et al.2000a), a review by Narayan et al and others (Narayan 2006a). T-wavealternans may reflect signal fluctuations from within the heart, whichmay also reflect calcium oscillations. Thus, detecting such fluctuationssuch as described above with respect to certain embodiments, likelyprovides a more robust method of predicting future arrhythmias thanT-wave alternans.

The above Figures illustrate biosignal analysis for early detection ofpotential arrhythmias. Another aspect of the invention focuses onstabilization of abnormal measured cell regulation detected by cardiacfluctuations and/or large fluctuations in the biological signal such aschanges in rate. In these embodiments, slower pacing rates or rhythmperturbations may be used which may help regain equilibrium in cellularhandling of calcium and/or other biochemicals. This is illustrated inFIGS. 8 and 9.

FIG. 8 illustrates how a metabolic process may fluctuate because therate is too fast to allow equilibrium to develop. Using conceptsdescribed with respect to FIG. 8, embodiments of methods of providingtherapy to patients are described. In some embodiments, the metabolicprocess may be uptake of calcium content into the sarcoplasmic reticulumof a heart cell. Treatment begins by slowing the heart rate to allow themetabolic process (such as calcium) to replenish its stores. Fasteractivations can then be delivered to approximate the desired averageheart rate, but for a short time to prevent re-emergence ofoscillations. The process can then be repeated.

The graph in FIG. 8 shows the dynamics of an example of an intracellularprocess. The cellular process shown in FIG. 8 is regulation of calciumcontent in the sarcoplasmic reticulum (SR) of a heart cell. During anormal heart beat, calcium is released from the SR and then pumped backinto the SR. As labeled, SR calcium content should remain between the 2top horizontal bars. Above this range, SR calcium overload may causeearly after depolarizations (EADs), which may trigger beats and causearrhythmias. Below this range, low SR calcium may create fluctuations(with periodicities ranging from every-other-beat to every third beat orother) and also cause arrhythmias.

At the left of the graph (label 810), rapid rate pacing (narrow spacingbetween arrows) has depleted SR calcium so that SR calcium fluctuationsare seen. In certain embodiments, therapy may now be initiated. This mayassist in replenishing SR calcium, and thereby prevent oscillations andthe effects of cellular derangements. Interval 820 of the graphillustrates slow pacing (wider spacing between arrows), which causes SRcalcium to rise rapidly to the normal range. Preferably, pacing is nottoo slow, as this may cause calcium overload (that is, above the topline) and initiate arrhythmias. Pacing may then be applied more rapidlyas shown in interval 830 of the graph to achieve the desired heart rate.If this is continued only for a few beats, SR calcium is not depletedand fluctuations do not re-occur. The cycle may then be repeated toapproximate the previous heart rate but without fluctuations in calcium

FIG. 9 illustrates embodiments of major electrical (pacing) modes oftreatment. These embodiments may be used to attenuate oscillations bypreventing cellular oscillations. Certain embodiments may also be usedto attenuate large fluctuations in response to rate (restitution) andthus attenuate signal fluctuations that indicate cardiomyopathy andpropensity for rhythm disorders.

Panel 910 shows a steep APD restitution relationship, which, asdescribed above, may be linked with arrhythmias in the atrium (AF) or inthe ventricle (ventricular tachycardia or ventricular fibrillation). Ifa tissue has this steep APD restitution relationship, then a prematurebeat (labeled X) that falls where APD slope >1 can cause wavebreak andfibrillation. This is also illustrated in FIG. 2C.

In certain embodiments, pacing modes to alter biosignal rate-response(such as APD restitution) that may be used include faster or slowerpacing to alter APD rate-behavior (restitution) to prevent beat X frominitiating fibrillation. For example, in panel 920, point A indicatesAPD of a baseline beat. Pacing at a slightly faster rate is applied tomove point A to point B, with a slightly shorter APD. However, continuedpacing at this faster rate moves the heart onto a differentrate-behavior (restitution) curve, that is lower and left-shifted (e.g.APD shortens, and the slope is steep only for very short diastolicintervals). In panel 920, this is indicated by shifting from point B topoint C. Now, the same premature beat X no longer falls on the steepportion of the rate-behavior (restitution) curve, and is less likely toinduce wavebreak and fibrillation (Weiss, Karma et al. 2006).Conversely, in some individuals (particularly with more severe;cardiomyopathy), slower activation rates may flatten the rate-response(restitution) curve. Accordingly, certain embodiments allow thisabnormality to be tracked so that therapy and heart rate can be tailoredaccordingly.

In certain embodiments, some drug treatments may be used to flatten orsteepen APD restitution, and thus alter the risk for action potentialfluctuations. For instance, administration of an agent to mimicadrenaline (e.g. isoproterenol) may result in similar steepening of APDrestitution as discussed above, which is likely due to calciumaccumulation in heart cells. Other drug treatments may be beneficial,such as beta-receptor-antagonists. In some embodiments, pacingstimulation of autonomic nerves may be performed. Such nerves lie in theinferior vena cava, superior vena cava and widely elsewhere within thebody (Schauerte, Scherlag et al. 2000a). They may be accessible bypacing in the neck and other regions. In addition, it is possible toalter activation in these nerves by behaviors such as swallowing,activation of the gastrointestinal tract, or coughing. Further, incertain embodiments pacing in complex irregular intervals may flattenrate-behavior (restitution), as seen from a close inspection of data byKalb et al. (Kalb, Dobrovolny et al. 2004). Beta-blockers and otherdrugs may also be used to flatten rate-behavior (restitution). Use ofsuch drugs has been shown to work in dogs by Hao et al. (Hao, Christiniet al. 2004), but no description of human rate-behavior (restitution)has been shown. Improving heart failure status may also makerate-behavior (restitution) curves less steep.

Notably, certain embodiments of described methods measure and indicateto the healthcare provider or the patient whether rate-response isfavorably or unfavorably altered by each of these interventions. Themeasurements and interventions can be repeated until the desired resultis achieved.

FIG. 10 depicts beat-to-beat biosignal fluctuations and therapytherefor. Panel 1020 shows fluctuations in biosignals (e.g. APD,unipolar electrogram activation-recovery-intervals) on consecutivebeats. In this example, these fluctuations occur every-other beat(“alternans”), although it should be noted that the same principlesapply to other beat oscillations (e.g. every 3^(rd) beat or every 4^(th)beat). Certain embodiments may disrupt this fluctuation by pacingout-of-phase to the initial fluctuations (arrows). That is, if theoriginal fluctuation is long-short, then short-long pacing may beapplied. In certain embodiments, the optimal strategy often does notneed to pace every beat. For example, in panel 1030, pacing beat 3shortens the biosignal (e.g. APD), and the oscillation has alreadybroken down by beat 4. Pacing on beat 5 again prevents lengthening ofthe biosignal.

Panel 1040 illustrates biosignal oscillations every 3^(rd) beat, butapplies to any odd-beat oscillations. Panel 1050 shows the effect ofout-of-phase pacing (arrows) that disrupts the original fluctuatingpattern. Pacing is generally applied to stimulate the heart before thenext natural beat is anticipated. This enables pacing to control thepattern and rate of the heart rhythm. The timing of the next paced beatis computed from the biosignal restitution curves (see above), toprevent further oscillations and disrupt present oscillations (examplesgiven in FIGS. 8 and 9).

In addition to therapy with pacing strategies, therapy by alteringstructure or function can be performed. This may involve ablation oftissue at sites where rate-response is steep, at sites where signalfluctuations arise, or sites where nerves may influence cardiacfunction.

FIGS. 11, 12A and 12B illustrate embodiments of a system and method foranalyzing biopotentials to develop a disease index and provideappropriate therapy in humans. In certain embodiments, the disease indexcan be calculated continuously during the day, or at specific times,including after treatment. FIGS. 11, 12A and 12B illustrate a variety ofsystem components and method steps for convenience of discussion andexplanation. It will be appreciated that embodiments of the inventionneed not incorporate all of the components or steps described withreference to these figures. Fundamentally, the various components andsteps of the systems and methods described below can be used implementthe sensing, analysis, and treatment modalities described above in aclinical or research setting.

Referring first to FIG. 11, in some embodiments of system 1105,electrical events in the heart 1110 are recorded with a combination ofsensing electrodes. These electrodes may be electrode catheters 1120placed within the chambers or vasculature of the heart. The electrodesmay include leads from an implanted pacemaker orcardioverter-defibrillator, which may be placed via the superior venacava or coronary sinus 1121. Electrodes can be located in proximity tothe nerves 1115 supplying the heart, which are also located in the leftatrium and ventricles (Scherlag, Nakagawa et al. 2005). In someembodiments of system 1105 electrodes can also be virtual (computed)electrodes from a computerized mapping system, ECG electrodes 1130,and/or stored electrograms of any database 1165. In certain embodimentsan ablation catheter 1125 placed within the heart or its vasculature canbe used to modify/destroy regions where signals indicate a high diseaseindex. The ablation catheter 1125 may interface with an ablation energygenerator 1160. The other electrodes may interface with an electrodecontroller 1140 and a pacing module 1150, all of which may communicatewith a process controller 1170. Ablation or pacing electrodes can bedirected to the locations of nerves 1115 supplying the heart.

The process controller 1170 may comprise various modules. These modulesmay include a sampling module 1135 to record the signal at various heartrates and a pacing module 1145 to provide additional heart rates forsampling the biosignal. As shown, Module 1175 is part I of an AnalyticEngine and may compute a disease index based on the change of thebiosignal with rate. Module 1180 is part II of the Analytic Engine andmay measure the rate-response of the biometric signal and determineswhether it is fluctuating. Module 1185 is the therapy module I and mayinterface with the pacing module to deliver therapy to disrupt biosignalfluctuations. Module 1190 is the therapy module II and may interfacewith an energy generator to modify structure via ablation (destruction)of tissue at sites where rate-response is steep, at sites where signalfluctuations arise, or sites where nerves 1115 may influence cardiacfunction. Module 1195 may comprise a display device and may provide aninteractive display of the progress of the computation. The output ofthe system may be shown on module 1195 and may include an assessment ofcardiac health (no fluctuations and absence of large signal variationswith rate) or risk (the opposite). The output may also indicate idealmodes of pacing therapy and of potential ablation therapy. The modulesmay be multifunctional; for example, the process controller may alsosample signals from other sources, such as cardiac motion from tissueDoppler imaging.

Some embodiments of system 1105 include an optimal sequence of actionsand modes to provide complete and efficient diagnosis and therapy in asemi-automated fashion. These embodiments may include one or morefunctional modes:

1. Sampling the biosignal;

2. Analytic Engine I: determining risk score from the rate-responserelationship of biosignal components;

3. Analytic Engine II: determining risk score from fluctuations inbiosignal components;

4. Therapy module I: disrupt fluctuations by pacing; and

5. Therapy module II: disrupt abnormal biosignal rate-response orfluctuations by tissue modification (e.g. ablation).

In certain embodiments of uses of system 1105 involving the heart, thesignal sampling mode uses one or more of ECG electrodes 1130 connectedto the body surface, electrode catheters 1120, 1122 in the heart 1110,an electrode 1155 in the esophagus, and/or virtual (computed)electrograms from a mapping system. In certain embodiments theseelectrodes remain stationary relative to the heart while sampling thecardiac event under investigation. In an alternative embodiment, signalscan be sampled retrospectively by uploading previously-storedelectrograms from the database 1165 to the processor controller 1170.Alternatively, signals can be reconstructed from cardiac motion (such astissue Doppler imaging), respiratory motion (such as on a respirator inthe intensive care unit), measured pulse oximetry, and/or otherbiometric signals.

In certain embodiments, the process controller 1170 directs the pacingmodule 1150 to stimulate the heart using electrodes 1120, 1121, 1122,1125 in the heart 1110, electrodes 1130 on the body surface, and/orelectrodes elsewhere such as in the esophagus (electrode 1155). Theelectrode controller 1140 may receive signals from the electrodesbefore, during and after pacing, and uses this information to increasethe range of heart rates available for signal sampling.

Some embodiments of system 1105 monitor that adequate contact ismaintained between the electrodes and the relevant tissue. For example,contact is monitored as a physician moves an electrode catheter orrotates, curves, or straightens the catheter in a region of the heart.In certain embodiments the degree of contact can be monitored by theprocess controller 1170 in various ways. For example, the processcontroller 1170 can ascertain contact by measuring variations in theamplitude of sensed signals. In another embodiment, the processcontroller 1170 can control the pacing module 1150 to emit pacingsignals through other electrodes, and use the amplitude of detectedpacing signals to ascertain contact. In yet another embodiment, theprocessing module 1170 can also determine contact by measuring tissueimpedance.

The wider the range of rates, at which each signal is sampled,particularly at faster rates, the more accurately the rate-behaviorcurve may be constructed for each signal by the Analytic Engine I. Byway of example, if the signal is ventricular it is first measured atresting rates (typically 60-80 beats/min). Certain embodimentsimplemented in an ambulatory device, may continue storing data to recordat rates >100 beats/min (such as exercise and stress) and <60 beats/min(such as rest and sleep). Such behaviors may exacerbate rhythm disorderssuch as AF in some patients. If pacing/stimulating the heart is anoption, some embodiments of system 1105 may increase heart rate bypacing to further expand the range of rates over which the biosignal ismeasured. Different pacing sequences may also be examined. As describedbelow, certain embodiments of system 1105 may empirically determine theactivation sequence needed to eliminate fluctuations. Accordingly,therapy can be tailored to an individual person's rate dynamics.

In the ventricle certain embodiments of system 1105 may be used to:

1. detect fluctuations in electrical signals;

2. detect fluctuations in mechanical signals (from echocardiography,magnetic resonance imaging, non-contact mapping or another modality);

3. compute rate-behavior of signal components, and detect a steeprelationship (marked variation in response to rate) or a shallowrelationship (minimal variation in response to rate);

4. quantify conductions within regions of the heart, and detectconduction slowing for early or late beats;

5. quantify preserved or attenuated sympathovagal activation; and/or

6. compute an index of metabolic balance (“health”) from said signalfluctuations in living patients.

The concept that fluctuations in clinically detectable variablesindicate lack of equilibrium in cellular mechanisms for disease isnovel. For instance, mechanical fluctuations (on echocardiography orblood pressure) indicate that calcium homeostasis may be unable to reachequilibrium under current conditions. This may involve cytosoliccalcium, sarcoplasmic reticulum calcium, release/reuptake dynamics orinter-related systems. Electrical fluctuations in the action potentialamplitude (or a surrogate) may also indicate dysregulation of calcium,of late sodium inactivation, or of transient outward current. Electricalfluctuations in action potential duration may indicate dysregulation ofpotassium or mechanisms related to calcium. Several other fluctuationsmay be detected, and this list is not intended to be exhaustive.

Fluctuations in the action potential amplitude may occur at slow rates,likely indicating calcium oscillations, which may predict ventriculararrhythmias and occur in patients with abnormalities of heart pumpfunction. Fluctuations in the rate and shape of the action potentialsmay thus indicate a wide variety of cellular abnormalities. Therapy canthus be tailored to an individual's heart signal fluctuations andcalibrated to the disease process and other co-morbidities in thatperson. Certain embodiments may track whether these and other therapies(for example, biventricular pacing) attenuate these markers.

In the atrium, certain embodiments of system 1105 may be used to:

1. detect fluctuations in electrical signals;

2. detect fluctuations in mechanical signals (e.g., fromechocardiography, non-contact mapping or another modality);

3. compute rate-behavior of signal components, and detect a steeprelationship (marked variation in response to rate) or a shallowrelationship (minimal variation in response to rate);

4. quantify conduction within regions of the heart, and detectconduction slowing for early or late beats;

5. quantify preserved or attenuated sympathovagal activation, includingsignal components reflecting ganglionic plexus innervation of the atria;

6. compute an index of metabolic balance (“health”) from said signalfluctuations in living patients.

Again, the concept that fluctuations in a clinical variable can indicatelack of equilibrium in cellular mechanisms is novel. As in theventricle, fluctuations in atrial action potential amplitude may occurat slow rates, again suggesting calcium oscillations, which may predictAF. These fluctuations are more likely to occur in patients with atrialabnormalities, and may represent an important component of the AFsubstrate. Fluctuations in the rate and shape of the action potentialsmay thus indicate a wide variety of cellular abnormalities. Therapy canthus be tailored to an individual's heart signal fluctuations andcalibrated to the disease process and other co-morbidities. Certainembodiment may thus track whether these or other therapies (for example,ablation, certain drugs) attenuate these markers.

As discussed, certain embodiments of system 1105 can thus detectpotentially detrimental effects of other treatments or activities. Forexample, right ventricular pacing may reduce left ventricular systolicfunction (David 2002). However, it is difficult to predict individualswho will suffer this effect. Certain embodiments of system 1105 canidentify such patients from fluctuations in human biosignals (actionpotentials, unipolar or bipolar electrograms from a device lead, orT-wave from the ECG). Autonomic innervation may also be tracked throughits effects on APD rate-behavior. Thus, sympathovagal stimulation thatfavors AF may make APD rate-behavior steeper, which may be tracked incertain embodiments. Worsening heart failure may lead to calciumoverload and other failed regulatory systems, causing signalfluctuations that may be detected in certain embodiments. Pro-arrhythmiafrom anti-arrhythmic and other medications (e.g. erythromycin) may bedetected in certain embodiments. Ischemia, both subclinical andclinically evident, may be detectable using ventricular signalfluctuations in certain embodiments. Inotropic therapy with dobutamineor milrinone can produce arrhythmias. This may be mediated by cellularchanges that can be tracked in certain embodiments.

Certain embodiments of system 1105 can also detect potential therapeuticbenefits. For example, biventricular pacing may improve heart failure(Bristow, Saxon et al. 2004). An early sign of improvement is reducedbiosignal fluctuations, such as electrical indices of action potentials(electrograms from implanted devices, or monophasic action potentials)reflecting calcium overload, which may be tracked in certainembodiments.

Further, autonomic effects can be tracked through effects on APDrate-behavior (restitution). Improved sympathovagal ‘balance’ thatprotects against atrial and ventricular arrhythmias may attenuaterate-response (making restitution more shallow), that can be tracked incertain embodiments. In addition, Beta-blocker and other neuro-hormonaltherapy may produce less marked rate behavior of electrical signals(restitution) and attenuate biosignal fluctuations which may be tracedin certain embodiments.

FIGS. 12A and 12B illustrate an embodiment of a method of determining arisk level of a patient for developing heart instability using certainembodiments of system 1105. In certain embodiments, the method of FIGS.12A and 12B analyzes electrical events in the heart via electrodes asdescribed with reference to FIGS. 1A-1C. In FIG. 12A, at step 1200, abiosignal may be detected at the electrodes described reference to FIGS.1A-1C.

This signal could come from a wide variety of sources. In oneembodiment, at a step 1205 the signal type of the detected biosignal maybe identified from a lookup table for a multi-input system. In otherembodiments the user may manually input the signal type beingmonitored/analyzed. The signal type identification also determines ifthe signal arises from the heart, brain, respiratory system,gastrointestinal tract, urogenital system, or some other source. Ifheart-related, the signal can be identified as a surface ECG,intracardiac, echocardiographic or other. If intracardiac, the signalcan be identified as an action potential (monophasic action potential orapproximated signal derived from contact pressure from a bipolarelectrode with wide bandpass filtering), bipolar electrogram, unipolarelectrogram, or some other appropriate signal type. The lookup table mayalso indicate that the identified biosignal is of a signal type made upof one or more components. In certain embodiments, the lookup table canbe a comprehensive biosignal inventory, with data on the distinctcomponents of each biosignal type and the physiological significance ofeach component. Each component of a biosignal may vary independentlywith rate and may fluctuate between beats. Each signal component mayreflect a distinct aspect of normal or abnormal physiology and thusreflect “high risk”. Below are examples of signal types and componentsthat may be included in the lookup table. However, the lookup table isnot limited to the examples below and may include signal types andcomponents for other heart-related signals, signals involving othermuscles (e.g. skeletal muscle, bladder and gastrointestinal tract),signal involving the brain, and/or signals involving the nervous system.

The signal may be an ECG with atrial components (P-wave, PR interval)and ventricular components (QRST waves). In certain embodimentsdescribed herein, for the atrium, it may be determined how P-waveduration varies with rate as a measure of atrial conduction slowing. Forthe ventricle it may be determined how the QT interval varies with rateas a measure of ventricular APD rate-behavior (restitution).

As described in detail above, the signal may be human action potentialsin the atrium and/or ventricle. The signals may be unipolar electrogramsfrom the human atrium and ventricle. Indeed, such signals convey many ofthe same information as the monophasic action potential. Moreover, withsufficient contact pressure and wide filter settings (for instance, 0.01to 500 Hz), a traditional electrode can record a signal very similar toa monophasic action potential. The signals may also be traditionalbipolar electrograms from the human atrium and ventricle. Certainembodiments of system 5 may determine rate response and fluctuations ineach component.

Other signals that can be handled by certain embodiments of system 5include electrical signals indicating brain wave activity, respiratoryactivity, and gastrointestinal activity, and signals measured fromautonomic ganglia as sympathetic nerve activity.

Between steps 1210 and 1240, the components of the signal may be parsed(processed stepwise). For a monophasic action potential (MAP), theparsed components may include phase 0 (upstroke), phase I, phase II,phase III and phase IV. For an ECG, the parsed components may includethe PR, QRS, QTU, ST, JT and JTU intervals. For example, the ECG signalmay be separated into atrial components (the P wave and PR interval),ventricular depolarization (the QRS complex), and ventricularrepolarization (the T wave). QRS complexes can be identified usingmethods discussed by Watanabe et al. (Watanabe, Bhargava et al. 1980)and U.S. Pat. No. 4,552,154 issued to Hartlaub, and U.S. Pat. No.6,035,231 issued to Sommo. Individual QRS complexes are then alignedusing one of several columnar techniques. Examples include methods thatalign electrograms about the point of largest positive or negativeslope, methods that align electrograms about their peak values, methodsthat minimize the mean square differences of the electrograms, andmethods based on metrics based on derived signals. T-waves may also beidentified and aligned similarly. Atrial activity may be considered tolie in the intervening intervals.

If the signal is an action potential (FIG. 1), phases 0, 1, 2 and 3 maybe separated. Components of interest of an action potential includedepolarization (phase 0), repolarization (phases 1-3), phase IIamplitude and action potential duration (time interval from phase 0 tophase 3). If the signal is a unipolar electrogram, it may be segregatedinto depolarization and repolarization phases. Each may be analyzed forthe waveform shape as well as duration. If the signal is a bipolarelectrogram, it may be segregated into depolarization and repolarizationphases. Each may be analyzed for the waveform shape as well as duration.

At step 1210, the next signal component of the signal is selected.Continuing at step 1215 the rate response of the selected component ismeasured at one or more rates and using one or more pacing sequences.Rate response may be determined for a wide range of observed heartrates. If available, pacing may also be used to increase the heart rateto provide a wider range of rates at which the component signal responseis measured to comprehensively assess rate response.

Certain embodiments may also use an adaptive series of programmedsequences to attenuate fluctuations. Such sequences may include a seriesof slow then fast beats tailored to the specific non-linear processesunder consideration. For example, suppose a heart rate of 100 beats/minis desired (cycle length 600 ms), but fluctuations arise above 90beats/min (cycle length shorter than 667 ms), suggesting derangements incalcium homeostasis. A sequence of 700 ms-700 ms-700 ms-700 ms-400ms-400 ms provides the desired average rate, but the first 4 beatsshould prevent fluctuations (by allowing replenishment of cellularcalcium stores). If stores are sufficiently replenished, the next twofast cycles (#5-#6) now may not give risk to fluctuations. The sequencecan then repeat. A variety of such sequences may be tested in step 1215,calibrated to whether fluctuations arise or are attenuated. These testsmay then be used to tailor therapy in later stages of certainembodiments of the described method.

At step 1220 the rate-response (“restitution”) curve may be constructedfor the selected component. Depending on the type of signal component,different calculations may be done to construct the restitution curve.Variations in monophasic action potential duration (time from phase 0 tothe phase III terminus) with rate may be calculated at step 1220. Thisis known as APD restitution (Franz, Swerdlow et al. 1988a). Restitutionfor additional components may also be calculated. For instance, the rateresponse (restitution) for phase 0 upstroke velocity may be calculatedas it is a measure of sodium channel fluctuations with rate (and restingmembrane potential), which may influence conduction slowing. Rateresponse of the amplitude of phase II of the action potential (or of aunipolar electrogram) may be calculated as it is a surrogate index ofcalcium, as mentioned above. If at step 1220 it is determined the slopeof the calculated restitution curve is greater than a threshold value,the process continues to a step 1225 where a risk score is incremented.For example, at step 1225 high risk may be assigned based on predefinedproperties of the biosignal rate-behavior (restitution) and the signaltype. For monophasic action potential duration (MAPD), risk may beranked higher at step 1225 if it is determined the rate-behavior(restitution) maximum slope >1 at step 1220 (Weiss, Karma et al. 2006).The risk score is described with greater detail below. The process thencontinues to step 1230. If at step 1220 it is determined that the slopeof the restitution curve is not greater than the threshold value, theprocess proceeds to step 1230.

At step 1230, it is determined whether the signal component fluctuates.For example, the biosignal may fluctuate at a native (baseline) heartrate. If it is determined the signal fluctuates, the process proceeds tostep 1235, where the risk score is incremented. For example, at step1235 a risk score of “Imminent risk” may be assigned if the biosignalfluctuates at native (baseline) heart rate. High risk may also beassigned at step 1235 if the signal fluctuates at the current heartrate. The process may then continue to step 1240. If at step 1230 it isdetermined the signal does not fluctuate, the process continues to step1240. At step 1240 it is determined if the selected signal component isthe last signal component of the biosignal (i.e., all the signalcomponents have been measured). If it is determined the selectedcomponent is not the last signal component, the process returns to step1210 where a signal component that has not previously been selected isselected to be measured. If it is determined the signal component is thelast signal component, the process proceeds to step 1243 illustrated inFIG. 12B.

Steps 1243 to 1275 may track cardiac status during therapy by measuringeach component of the biosignal. Signal analysis and risk assignment maybe performed before intervention at steps 1210-1240 and may be repeatedafter an intervention at steps 1243-1275. As before, each signalcomponent may be processed for rate response.

At step 1243, a signal component of the biosignal is selected to bemeasured. Continuing at step 1245, biosignal variations may be assessedfor all heart rates observed and, if available, by pacing at fasterheart rates and at various sequences as described above. The effect ofthe intervention may then be determined. Further, at step 1255 it isdetermined if rate response (restitution) is steeper than it was beforethe intervention. If it is determined that the slope is greater thanbefore intervention, the process may proceed to a step 1260, where therisk score is flagged as “increased risk” and/or the intervention isflagged as “detrimental intervention. The process then proceeds to step1265. If at step 1255 it is determined the restitution is not steeperthan previously measured, the intervention may not be detrimental andthe process proceeds to step 1265.

At step 1265 it is determined if the biosignal fluctuates more thanbefore treatment or fluctuates when previously it did not. If at step1265 it is determined that the signal fluctuation has increased, theprocess proceeds to a step 1270 where the risk score is flagged as“increased risk” and/or the intervention is flagged as “detrimentalintervention.” The process then proceeds to step 1275. If at step 1265it is determined the signal fluctuation has not increased, theintervention may not be detrimental and the process proceeds to step1275. At step 1275 it is determined if the selected signal component isthe last signal component of the biosignal (i.e., all the signalcomponents have been measured). If it is determined the selectedcomponent is not the last signal component, the process returns to step1243 where a signal component that has not previously been selected isselected to be measured. If it is determined the signal component is thelast signal component, the process ends.

In some embodiments, ECG and electrogram data may be uploaded from adatabase 160 for analysis in an analogous fashion to the describedreal-time mode of operation. Data from the database can be from the sameor different patients, recorded at any time and using any acquisitionsystem.

Analytic Engine I 1175 in certain embodiments may be implemented insoftware. Certain embodiments of Analytic Engine I 1175 operate quicklyand are suitable for real-time as well as off-line analysis.

In certain embodiments, steps 1220-1225 of the process of FIG. 12A maybe carried out by Analytic Engine I 1175. For example, Analytic Engine I1175 may determine the rate-response (restitution) of each component.

Certain embodiments of the risk scoring system may be based on APDrate-behavior (restitution), alternans, and/or conduction rate-behavior(restitution) (Analytic Engine I). Certain embodiments of system 1105may initially use a “default” mode of risk scoring. Some of theseembodiments may also have an adaptive design that may tailor both riskassignment and therapy to observed fluctuations and rate response in theindividual.

In certain embodiments of system 1105, steps 1230-1235 may be carriedout by the Analytic Engine II 1180. Analytic Engine II 1180 maydetermine whether biosignals or their components fluctuate.

Elimination of signal fluctuations using certain embodiments of system1105 may prevent AF onset. It has been found in some cases that rapidatrial pacing did not cause AF. Notably, in some of these cases,conduction slowing arose near the pacing site at very fast rates.Patients with less significant disease may show conduction slowing onlyat very fast rates, while more significant disease may produce morecomplex effects. In some cases, signal fluctuations are present (at slowrates), yet are attenuated by conduction slowing, which causes localcapture block and thus prevents AF.

As discussed above, signal fluctuations indicate disease risk.Fluctuations at fast rates may occur in persons with low/intermediaterisk (and minimal/intermediate cellular or structural disease).Conversely, fluctuations at slow rates may be seen in patients withsubstantial disease and greater substrate. The data presented shows thisfor the ventricle and atrium.

Embodiments of methods that may be performed using embodiments of system1105 include pacing methods to modulate the shape of the rate-behavior(restitution) curve of electrical signals, or to disrupt fluctuations inthe biosignal, and thus normalize abnormal calcium handling and preventprogression of contractile dysfunction and/or arrhythmia.

In certain embodiments; the process controller 1170 controls the pacingmodule 1150, to stimulate the heart using electrodes 1120, 1122 in theheart, electrodes 1130 on the body surface, and/or electrodes elsewheresuch as from the esophagus (electrodes 1155). The electrode controller1140 receives signals from the electrodes before, during and afterpacing. Pacing may be used to increase heart rate and introduce extrabeats to alter biosignal rate-response and disrupt biosignalfluctuations.

Pacing can be applied via any electrode. In certain embodiments, pacingmay be applied via implanted electrodes of a pacemaker or implantedcardioverter-defibrillator in the outpatient setting, or from anablation catheter 1125 at electrophysiology studies. Pacing techniquesincluding burst pacing and multiple extra stimuli can be used. Thepacing stimulus may be monophasic, biphasic, or triphasic.

Certain embodiments of system 1105 use pacing to modify therate-response (i.e. restitution) of the biosignal or to disrupt thepattern of biosignal fluctuations. Flattening of rate-behavior(restitution) or attenuation of fluctuations may reduce the propensityto arrhythmias.

Certain embodiments of system 1105 may utilize an adaptive pace sequenceapproach to eliminate signal fluctuations. Sequences may be tailored tothe individual patient, and may be modified at different times. Bymonitoring electrical signal fluctuations in real-time, certainembodiments of system 1105 can iteratively solve for the optimum pacesequence. Iterations can commence from basic “defaults” for eachpatient, established as part of Analytic Engine II 1180.

Other cellular derangements can be addressed by tailored pacing in thisfashion, titrated to the relevant component of electrical fluctuations.Such pacing may prevent the sequelae from cellular derangements,including the onset of heart rhythm disorders and progression ofcardiomyopathy.

From a cell mechanism point of view, the methods that may be implementedby system 1105 may be viewed as follows:

A. Design/quantify cycle periodicity

a. If varying on an even beat basis (every fourth or secondbeat)—intervene on odd beats;

b. If varying on an odd beat basis (every third or fifth beat)—interveneon even beats;

B. Determine Individualized Rate thresholds for different CellularProcesses (e.g. heart) by progressively increasing rate until:

a. AP Phase I oscillations occur signaling incomplete recovery ofI_(Na);

b. AP Phase II oscillations occur signaling abnormal calcium handling,potentially abnormal I to kinetics, and/or abnormal late sodiuminactivation kinetics;

c. AP Phase III oscillations occur, which involves potassium currents,such as I_(K), and is often dependent upon the APD restitution. Thisoperates at fast rates.

C. Calibrate against disease based on the measurable effects of ionchannels

a. In other tissues, such as the brain, the measurable effects of otherion channels can be calibrated against disease. For instance, variationsin I_(Na), I_(K) can be measured and modeled against observedepileptiform activity.

b. In the atrium, P-P intervals may be varied to ameliorate calciumoverload on some cycles to reduce AP shape variations.

c. In the ventricle, the R-R intervals may be varied to amelioratecalcium overload on some cycles to reduce AP shape variations. PRintervals may also be varied, thus varying R-R intervals to amelioratecellular metabolic dynamics and AP shape variations. This variation maysupport the beneficial effect of certain irregular rhythms (e.g.,bigeminy or trigeminy) which are not pro-arrhythmic in atrium orventricle.

Certain embodiments of system 1105 may use a clinical measure toindicate cellular health. In one embodiment, calcium fluctuations thatindicate a cellular manifestation of cardiomyopathy are determined. Asshown above, fluctuations in the action potential shape (particularlyphase II) may indicate oscillations in cellular calcium. Suchoscillations, in turn, may occur after calcium overload as described incardiomyopathy. Thus, certain embodiments of system 1105 may provide aclinical index that probes cardiomyopathy from oscillations in cardiacsignals (particularly phase II of action potentials).

Certain embodiments of system 1105 may indicate to the healthcareprovider or patient whether fluctuations are favorably or unfavorablyaltered by interventions. This may be repeated until the fluctuationsare attenuated or abolished.

Certain embodiments of system 1105 are also designed to stimulatecardiac nerves to modulate their impact on heart functioning. Forinstance, it is known that elevated sympathovagal balance canprecipitate AF in animals (Patterson, Po et al. 2005). System 1105 mayperform electrical or other energy source stimulation of the atrium orventricle to alter the biosignal rate-behavior or the presence ofbiosignal fluctuations.

In some embodiments, Therapy Module II 1185 may operate during invasiveelectrophysiology studies, and may modify tissue structure where thebiosignal rate-response is steep, or where biosignal fluctuations occur.This may be achieved by ablating tissue, by altering it via heating orcooling, and/or by using electromagnetic fields.

In certain embodiments, the energy generator 1160 may be activated toapply energy (radiofrequency, infrared, cryoablation, microwaveradiation, or other energy) via the ablation electrode 1125. Theelectrode 1125 can be moved within the heart manually by an operator, orremotely using robotic or computer assisted guidance. In certainembodiments, Therapy Module II 1185 may only be actuated after pacinginterventions have failed to attenuate the steepness of rate-behavior(restitution).

In certain embodiments, after ablation, system 1105 remotely moves thecatheter 1125, or prompts the user to move the catheter 1125, to anadjacent location to assess its rate response. If rate-behavior(restitution) is steep, or fluctuations are observed, ablation may berepeated. This may result in elimination of tissue where steeprate-behavior (restitution) or fluctuations are observed (bothindicating diseased tissue and/or cardiomyopathy, see FIGS. 2A-4). Inthe certain embodiments, system 1105 can engage remote catheternavigation to survey the entire atrium for such fluctuations.

Certain embodiments of system 1105 may also be designed to ablatecardiac nerves to modulate their impact on heart functioning.Accordingly, certain embodiments of system 1105 may use radiofrequencyor other energy source to alter the biosignal rate-behavior or thepresence of biosignal fluctuations.

Permanent tissue modification may not be necessary in somecircumstances.

Accordingly, in certain embodiments, subthreshold pacing may be applied,or an electromagnetic field may be used to modulate tissue function fora desired period of time. The type of treatment used may be modulated inreal-time depending upon risk assessment (from signal restitution andfluctuations).

The above described approach of introducing “planned” rhythmirregularities is novel and appealing. Although such irregularities mayappear potentially detrimental, it should be noted that many naturallyobserved “regular irregularities” are benign (i.e., not dangerous), suchas atrial or ventricular bigeminy (where every-other-beat is fasterand/or from a different source) and normal fluctuations in heart rate(“sinus arrhythmia”). Similarly, slow rates are central to beta-blockertherapy, which is widely accepted to improve health at body, organ, andcellular levels.

Certain embodiments also predict which individuals will develop AF anddeliver therapy that carefully modulates atrial function to reduce thesefluctuations and prevent the development of AF.

The embodiments above described in relation to heart-related electricalsignals. However, one of ordinary skill in the art will recognize thatsimilar embodiments may be used for analyzing other electrical signals.Additional embodiments may be used for striated muscle (peripheralmuscle), smooth muscle in the gastrointestinal, urogenital andrespiratory systems, and in association with the electroencephalogram orinvasive surgical techniques such as open heart or brain surgery.

One embodiment analyzes action potentials or surrogate signals ofactivation and recovery from the beating heart. These signals may bedetected from an ECG, implanted pacing electrodes (in the heart or othertissues via far-field detection), an echocardiogram (that indicatesactivation and recovery), or other sources. In certain embodiments,pacing and ablation electrodes can be used within the beating heart andin nerves that supply the heart. Other embodiments may use signalsderived from heart contraction, including reflected sound waves orelectrical impedance changes. Certain embodiments may analyze signalsand apply pacing to skeletal muscle in individuals with musculardiseases. Some embodiments may analyze signals and apply treatment tothe gastrointestinal tract. Other embodiments may apply treatment to themuscles of the respiratory system.

Certain embodiments calculate the changes in response to rate (alsoknown as “restitution”) of electrical heart signals, and fluctuations insaid signals, to calculate a risk index for abnormal rhythms such asatrial fibrillation (AF; in the top chambers, or atria) or ventricularfibrillation (VF; in the bottom chambers, or ventricles). As describedbelow, steep slope of the rate-behavior or fluctuations may reflect “asick heart,” or other metabolic abnormalities that may indicate apredisposition to AF in the atria or VT/VF in the ventricles. Certainembodiments can use action potentials (such as monophasic actionpotentials or other surrogates such as electrograms from implantedatrial leads) from the human heart to construct this risk index. Someembodiments may use unipolar or bipolar electrograms and/or signalsrepresenting cardiac motion from detailed echocardiographic, CT, ormagnetic resonance imaging.

If fluctuations are observed, or expected from the rate-response(restitution) curve, certain embodiments may apply therapy to disruptthe pattern of fluctuations or modify restitution. This therapy mayattenuate cellular derangements in cardiomyopathy and potentially reducethe risk of arrhythmias.

Certain embodiments can thus determine if cardiac disease(cardiomyopathy) is progressing—due to native disease or as aside-effect of potentially detrimental interventions such as rightventricular pacing, or inotrope therapy (e.g. with dobutamine). Certainembodiments may use ablation to modify tissue that exhibits abnormalfluctuations. Therapy can also be applied to nerves that regulate heartfunction and may cause rhythm disorders. Certain embodiments can tracksuch therapy. Certain embodiments may also track progression of cardiacdisease from biventricular pacing, after ablation (for atrialfibrillation or ventricular tachycardia), beta-blocker therapy, etc.

In one embodiment, stimulation of the top chamber of the heart (atria)or bottom chamber (ventricle) can be performed. The stimulation assistsin cellular regulation of the targeted deranged metabolic (or ionic)process. Accordingly, certain embodiments may attenuate heart signaloscillations that occur due to abnormal calcium homeostasis. Forexample, in certain embodiments, slow rate pacing may first be appliedto the heart to restore sarcoplasmic reticulum calcium stores. Theduration of slow pacing may be titrated carefully, to avoid calciumoverload that produces extra beats (from after depolarizations) andtriggers arrhythmias. After this period of relative calcium loading, aperiod of faster pacing may be used to achieve the desired average heartrate. Again, this may be titrated carefully to avoid calcium depletionthat may again produce oscillations. The reason for this rate variationis that calcium sequestration and release kinetics are non-linear. Incertain embodiments the impact of rate variations on action potentialduration (via restitution) is also considered.

In other embodiments, methods of treatment are performed includingincreasing or reducing the activation rate, and directly disrupting theregularity of oscillation. For example, if fluctuations occur everythird beat, in certain embodiments the apparatus may alter activation ofthe atrium or ventricle every fourth or second beat—out of phase withthe native oscillation—to disrupt the fluctuations.

In some embodiments, destruction (ablation) may be targeted at tissueresponsible for fluctuations or abnormal rate-behavior (which leads tofluctuations) of biological signals. Ablation may include, but is notlimited to, conventional sources (radiofrequency energy, cryoablation),and alternative sources, including microwave, ultrasound and externalbeam irradiation.

In yet other embodiments, function of the tissue responsible for theheart signal fluctuations may be altered using external electromagneticfields, pacing at a subthreshold intensity, and/or other interventions.

Atrial cardiomyopathy (heart failure of the top chamber of the heart)may be detected and tracked in certain embodiments. Certain embodimentsdetect signatures of atrial cardiomyopathy. Certain embodiments mayprovide methods for attenuating derangements in the intracellularhandling of calcium and other ions due to cardiomyopathy. Theattenuation reduces the severity of atrial cardiomyopathy (bypotentially reducing contractile dysfunction) as well as “side-effects”such as heart rhythm disorders (atrial fibrillation). Certainembodiments provide a method for tracking atrial cardiomyopathy inhumans. Certain embodiments described herein calculate an index, in theintact human heart, of specific abnormalities in atrial cell function(“cell health”) that indicate atrial cardiomyopathy and risk forside-effects including atrial fibrillation. This index can be used totrack whether these abnormalities improve or worsen with therapy. Theindex can also be used to analyze atrial action potentials, a surrogatesignal from a catheter, signals from an implanted atrial lead (from apacemaker or defibrillator), signals from echocardiography, and/orsignals from the electrocardiogram.

Certain embodiments described herein detect specific abnormalities inatrial cell function (“cell health”) that likely cause heart rhythmdisorders and which may represent early forms of atrial cell disease(cardiomyopathy). These abnormalities may be present at heart rateseasily found during the activities of daily living, yet becomeexaggerated just prior to AF. Thus, certain embodiments can be used tocontinuously track the propensity for AF during therapy and then delivertherapy if the propensity (signal oscillations) is observed.

Certain embodiments described herein target specific cellularabnormalities in regulation of atrial cell physiology, which may becentral to weakened atrial contraction and the initiation of AF. Thus,ameliorating this disequilibrium may improve atrial cardiomyopathy aswell as risk for AF and other sequelae. As a result, certain embodimentsare configured to improve and reverse abnormal features of atrialcardiomyopathy and may use methods described herein to track whether thetherapy is working.

Certain embodiments herein introduce a paradigm shift in assessingventricular cardiomyopathy. Because they may assess, detect, and actupon the biochemical balance of ventricular cells, guided by homeostaticmechanisms that are very likely central both to contractile (mechanical)and heart rhythm abnormalities in human beings in real time, they candynamically indicate cellular health and disease risk and guide therapyto normalize these abnormalities. In animal experiments, cellularcalcium balance provides a precise measurement of ventricular cellfunction and the risk for VT/VF (Laurita and Rosenbaum 2008).Embodiments of methods are described herein that significantly improveupon existing methods for detecting risk for worsening ventricularcardiomyopathy or risk for VT/VF. These embodiments can be used toanalyze ventricular action potentials, signals from a ventricular lead,signals from echocardiography or signals from the electrocardiogram.

Certain embodiments of methods may focus on oscillations in actionpotential shape (see below, particularly phase II). Examining actionpotential shape is a new field, since most or all prior art examinesaction potential duration. Some methods described herein may focus onfluctuations in action potential shape, which may indicate cellularimbalances (disequilibrium) in calcium and other metabolic processes, aschanges in action potential duration may not be sensitive to suchfluctuations. Certain embodiments of methods described herein measureslow rate oscillations and provide potential therapy.

Embodiments described herein link the risk for VT/VF with cellularabnormalities that are detectable from the beating heart in individuals.Unlike prior methods, whose association with VT/VF is indirect, certainembodiments described herein assess specific abnormalities inventricular cell function (“cell health”) caused by ventricularcardiomyopathy which explains why VT or VF initiates at a cellularlevel. Some such embodiments may measure these fluctuations in theaction potentials of ventricular cells. Furthermore, in certainembodiments these fluctuations may be tracked to determine if therapy iseffective.

Embodiments described herein may use novel pacing strategies, and othertechniques, to assist the heart in normalizing cellular homeostasis (incalcium and other metabolic processes). Treatment may be tailored toeach patient. Certain embodiments may be used to detect attenuation offluctuations in action potentials (or surrogate signals) providing amethod to determine if cardiac resynchronization therapy is improvingheart failure at the tissue and cellular level. Similarly, certainembodiments can detect exaggerated fluctuations if heart failure isworsening (such as from right ventricular pacing). In certainembodiments, after treatment of these abnormalities, these fluctuationscan be continuously tracked as a marker of cellular health.

The implementation of the systems and methods described herein is basedlargely upon digital signal processing techniques. However, it should beappreciated that a person of ordinary skill in this technology area caneasily adapt the digital techniques for analog signal processing.

Those of skill will recognize that the various illustrative logicalblocks, modules, circuits, and algorithm steps described in connectionwith the embodiments disclosed herein may be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in an ASIC. The ASIC mayreside in a user terminal. In the alternative, the processor and thestorage medium may reside as discrete components in a user terminal.

While the above detailed description has shown, described, and pointedout novel features of the invention as applied to various embodiments,it will be understood that various omissions, substitutions, and changesin the form and details of the device or process illustrated may be madeby those skilled in the art without departing from the scope of theinvention. As will be recognized, the invention may be embodied within aform that does not provide all of the features and benefits set forthherein, as some features may be used or practiced separately fromothers. The scope of the invention is indicated by the appended claimsrather than by the foregoing description. All changes which come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

1. A method for assessing a risk associated with a suspected heartrhythm disorder in a beating heart, the method comprising: detecting,using one or more sensors, one or more signals from the beating heartfor a plurality of time segments; pacing the beating heart for a portionof the plurality of time segments to modify at least one of rate orregularity of the beating heart; calculating, via a computer processor,fluctuations in at least one of signal shape, duration of signalcomponents, signal amplitude and signal characteristics between a firsttime segment and a second time segment of the plurality of timesegments, wherein the second time segment is selected from the portionof the plurality of time segments during which pacing occurs;generating, via the computer processor, a risk score for the heartrhythm disorder determined based at least in part on the fluctuations;and displaying on a display device in communication with the computerprocessor a clinical representation based at least in part on the riskscore.
 2. The method of claim 1, wherein the signals comprise actionpotentials and the fluctuations comprise changes in a slope of an actionpotential restitution curve.
 3. The method of claim 2, wherein theaction potential duration restitution curve is defined by actionpotential durations corresponding to preceding diastolic intervalsassociated with the one or more signals
 4. The method of claim 2,wherein the slope of the action potential duration restitution curveexceeds a threshold.
 5. The method of claim 4, wherein the threshold isa value of one.
 6. The method of claim 1, further comprising detectingconduction slowing.
 7. The method of claim 6, wherein the conductionslowing affects at least one of the fluctuations calculated in responseto the pacing.
 8. The method of claim 1, wherein pacing comprisesprogressively increasing pacing to determine oscillations in atrialsignals prior to onset of atrial fibrillation.
 9. The method of claim 1,wherein pacing is increased or decreased to prevent a beat frominitiating atrial fibrillation.
 10. The method of claim 1, whereinpacing is applied by one or more of an implanted pacemaker electrode, animplanted cardioverter-defibrillator electrode, and an ablationcatheter.
 11. The method of claim 1, further comprising ablating tissueof the beating heart to modify the at least one of the tissue structureand function.
 12. The method of claim 11, further comprising, afterablating, repeating the steps of pacing, calculating, generating anddisplaying until the fluctuations are attenuated or abolished.
 13. Themethod of claim 11, wherein ablating tissue comprises one or more ofapplying radiofrequency, infrared, cryoablation, microwave radiation, orother energy to tissue of the beating heart.
 14. The method of claim 1,wherein the fluctuations are calculated between alternate beats of thebeating heart.